Search Results for author:
Found 25 papers, 4 papers with code
Single Stage Adaptive Multi-Attention Network for Image Restoration
In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring.
Ranked #1 on
Image Denoising
on DND
Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review
This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis.
Leveraging GANs for data scarcity of COVID-19: Beyond the hype
These recommendations might be useful to improve acceptability for the GAN-based approaches for data augmentation as GANs for data augmentation are increasingly becoming popular in the AI and medical imaging research community.
Brain Tumor Synthetic Data Generation with Adaptive StyleGANs
Furthermore, the model can generate high-quality synthetic brain MRI with a tumor that can limit the small sample size issues.
Spot the fake lungs: Generating Synthetic Medical Images using Neural Diffusion Models
In this work, we explore the possibilities of synthesis of medical images using neural diffusion models.
Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data
Specifically, early fusion was the most used technique in most applications for multimodal learning (22 out of 34 studies).
Leveraging machine learning for less developed languages: Progress on Urdu text detection
We present the use of machine learning methods to perform detection of Urdu text from the scene images.
Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition
Robust speech emotion recognition relies on the quality of the speech features.
Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices
We selected U-Net because, in medical image segmentation, U-Net is a prominent model that provides improved performance for medical image segmentation even if the dataset size is small.
R2U++: A Multiscale Recurrent Residual U-Net with Dense Skip Connections for Medical Image Segmentation
The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder.
Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review
This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data.
Urdu text in natural scene images: a new dataset and preliminary text detection
To the best of our knowledge, the work is the first of its kind for the Urdu language and would provide a good dataset for free research use and serve as a baseline performance on the task of Urdu text extraction.
Coconut trees detection and segmentation in aerial imagery using mask region-based convolution neural network
Food resources face severe damages under extraordinary situations of catastrophes such as earthquakes, cyclones, and tsunamis.
A deep learning pipeline for identification of motor units in musculoskeletal ultrasound
In this work, we present an alternative method - a deep learning pipeline - to identify active MUs in ultrasound image sequences, including segmentation of their territories and signal estimation of their mechanical responses (twitch train).
Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks
In our work, the generator of the GAN is trained to generate a segmented mask of a given input CXR.
Human Activity Recognition using Multi-Head CNN followed by LSTM
Achieving high accuracy by traditional machine learning algorithms, (such as SVM, KNN and random forest method) is a challenging task because the data acquired from the wearable sensors like accelerometer and gyroscope is a time-series data.
Pioneer dataset and automatic recognition of Urdu handwritten characters using a deep autoencoder and convolutional neural network
More specifically, we use a two-layer and a three-layer deep autoencoder network and convolutional neural network and evaluate the two frameworks in terms of recognition accuracy.
Seizure Prediction Using Bidirectional LSTM
The purpose of this work is to investigate the application of bidirectional LSTM for seizure prediction.
Understanding and Partitioning Mobile Traffic using Internet Activity Records Data -- A Spatiotemporal Approach
The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior.
An Intelligent Monitoring System of Vehicles on Highway Traffic
The proposed method uses an HD (High Definition) camera mounted on the road side either on a pole or on a traffic signal for recording video frames.
KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features
This paper presents a recognition system for handwritten Pashto letters.
3D human action analysis and recognition through GLAC descriptor on 2D motion and static posture images
We then characterize the action video by extracting the Gradient Local Auto-Correlations (GLAC) features from the SHIs and the MHIs.
Generative Adversarial Network for Medical Images (MI-GAN)
The proposed model achieves a dice coefficient of 0. 837 on STARE dataset and 0. 832 on DRIVE dataset which is state-of-the-art performance on both the datasets.
Supervised classification for object identification in urban areas using satellite imagery
In this study gray-scale images are used for training the classification model.
Call Detail Records Driven Anomaly Detection and Traffic Prediction in Mobile Cellular Networks
By passing anomaly and anomaly-free data through this model, we observe the effect of anomalous activities in training of the model and also observe mean square error of anomaly and anomaly free data.
